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000724540 019__ $$a899563508$$a908086671
000724540 020__ $$a9783319126104$$qelectronic book
000724540 020__ $$a3319126105$$qelectronic book
000724540 020__ $$z9783319126098
000724540 020__ $$z3319126091
000724540 0247_ $$a10.1007/978-3-319-12610-4$$2doi
000724540 035__ $$aSP(OCoLC)ocn896825016
000724540 035__ $$aSP(OCoLC)896825016$$z(OCoLC)899563508$$z(OCoLC)908086671
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000724540 049__ $$aISEA
000724540 050_4 $$aQ327
000724540 08204 $$a006.4$$223
000724540 1112_ $$aInternational Conference on Pattern Recognition$$n(2nd :$$d2013 :$$cBarcelona, Spain)
000724540 24510 $$aPattern recognition applications and methods$$h[electronic resource] :$$bInternational Conference, ICPRAM 2013, Barcelona, Spain, February 15-18, 2013, Revised selected papers /$$cAna Fred, Maria De Marsico, editors.
000724540 2463_ $$aICPRAM 2013
000724540 264_1 $$aCham :$$bSpringer,$$c2015.
000724540 300__ $$a1 online resource (xv, 312 pages) :$$billustrations.
000724540 336__ $$atext$$btxt$$2rdacontent
000724540 337__ $$acomputer$$bc$$2rdamedia
000724540 338__ $$aonline resource$$bcr$$2rdacarrier
000724540 4901_ $$aAdvances in Intelligent Systems and Computing,$$x2194-5357 ;$$vvolume 318
000724540 500__ $$aIncludes author index.
000724540 504__ $$aReferences
000724540 5050_ $$aPreface; Organization; Contents; Part I Theory and Methods; A Two-Part Approach to Face Recognition: Generalized Hough Transform and Image Descriptors; 1 Introduction; 2 Method; 2.1 Modified GHT; 2.2 Gradient Distance Descriptor; 3 Results and Discussion; 4 Conclusions; References; Improved Boosting Performance by Explicit Handling of Ambiguous Positive Examples; 1 Introduction; 1.1 Relation to Bootstrapping Methods; 1.2 Contributions; 2 Relation to Previous Work; 3 Boosting Theory; 3.1 Convex-Loss Boosting Algorithms; 3.2 Robust Boosting Algorithms; 4 A Two-Pass Exclusion Extension
000724540 5058_ $$a4.1 Inverted Cascade5 Experiments; 6 Results; 6.1 Comparison of Boosting Algorithms; 6.2 Bootstrapping Methods in Relation to Outlier Exclusion; 7 Discussion and Future Work; 8 Conclusions; References; Discriminative Dimensionality Reduction for the Visualization of Classifiers; 1 Introduction; 2 Supervised Visualization Based on the Fisher Information; 2.1 Computation of the Class Probabilities; 2.2 Approximation of Minimum Path Integrals; 3 Training a Discriminative Visualization Mapping; 4 Visualization of Classifiers; 5 Conclusions; References
000724540 5058_ $$aOnline Unsupervised Neural-Gas Learning Method for Infinite Data Streams1 Introduction; 2 Related Work; 3 Proposed Algorithm (AING); 3.1 General Behaviour; 3.2 AING Distance Threshold; 3.3 AING Merging Process; 4 Experimental Evaluation; 4.1 Experiments on Synthetic Data; 4.2 Experiments on Real Datasets; 5 Conclusions and Future Work; References; The Path Kernel: A Novel Kernel for Sequential Data; 1 Introduction; 2 Kernels and Sequences; 2.1 Sequence Similarity Measures; 3 The Path Kernel; 3.1 Efficient Computation; 3.2 Ground Kernel Choice; 4 Experiments; 5 Conclusions; References
000724540 5058_ $$aA MAP Approach to Evidence Accumulation Clustering1 Introduction; 2 Probabilistic Model; 3 Optimization Algorithm; 3.1 Computation of a Search Direction; 3.2 Computation of an Optimal Step Size; 3.3 Complexity; 4 Related Work; 5 Experiments and Results; 5.1 UCI and Synthetic Data; 5.2 Text Data; 6 Conclusions; References; Feature Discretization with Relevance and Mutual Information Criteria; 1 Introduction; 1.1 Our Contribution; 2 Background; 2.1 Entropy and Mutual Information; 2.2 Feature Discretization; 2.3 Unsupervised Discretization; 2.4 Supervised Discretization; 3 Proposed Methods
000724540 5058_ $$a3.1 Relevance-Based LBG3.2 Mutual Information Discretization; 4 Experimental Evaluation; 4.1 Comparison Between Our Approaches; 4.2 Comparison with Existing Methods; 5 Conclusions; References; Multiclass Semi-supervised Learning on Graphs Using Ginzburg-Landau Functional Minimization; 1 Introduction; 2 Data Segmentation with the Ginzburg-Landau Model; 2.1 Application of Diffuse Interface Models to Graphs; 3 Multiclass Extension; 3.1 Generalized Difference Function; 3.2 Computational Algorithm; 4 Results; 4.1 Synthetic Data; 4.2 Image Segmentation; 4.3 Benchmark Sets; 5 Conclusions
000724540 506__ $$aAccess limited to authorized users.
000724540 520__ $$aThis book contains the extended and revised versions of a set of selected papers from the 2nd International Conference on Pattern Recognition (ICPRAM 2013), held in Barcelona, Spain, from 15 to 18 February, 2013. ICPRAM was organized by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC) and was held in cooperation with the Association for the Advancement of Artificial Intelligence (AAAI). The hallmark of this conference was to encourage theory and practice to meet in a single venue. The focus of the book is on contributions describing applications of Pattern Recognition techniques to real-world problems, interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance Pattern Recognition methods.
000724540 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed January 16, 2015).
000724540 650_0 $$aPattern perception$$vCongresses.
000724540 650_0 $$aPattern recognition systems$$vCongresses.
000724540 7001_ $$aFred, Ana,$$eeditor.
000724540 7001_ $$aDe Marsico, Maria,$$eeditor.
000724540 77608 $$iPrint version:$$z3319126091$$z9783319126098
000724540 830_0 $$aAdvances in intelligent systems and computing ;$$vvolume 318.
000724540 852__ $$bebk
000724540 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-12610-4$$zOnline Access$$91397441.1
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000724540 980__ $$aBIB
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